1. Field of the Invention
The present invention relates to the field of computer-based auction design and analysis processes. Specifically, the present invention relates to a method and system for setting an optimal price preference policy for an auction.
2. Related Art
Modern electronic forum based auctions, such as World Wide Web and other Internet based auctions have complex rules with varied and observable characteristics and situations, as well as unobservable structural elements. Auction participants, either sellers or buyers, must make a number of decisions relating to the auction.
Sellers, for example, conducting an auction to sell an item, can improve the auction outcome in their favor by treating bidders with identifiable differences differently. Buyers, correspondingly conducting an auction to buy an item, can also improve the auction outcome in their favor by treating bidders with identifiable differences differently.
Participants entering a market, such as bidders in an auction, differ greatly across a wide spectrum of dimensions. Other market participants, large scale purchasers such as governments or large scale sellers such as major corporations, for example, deal with different bidders entering a market in a variety of differing ways. Illustratively, the United States government offers a 6% price preference for domestically produced U.S. products under legislation mandating what is commonly known as a “Buy-American” policy. Governments of the various states and of other nations have similar policies.
The operation of these price preference policies may be illustrated by the following example. The U.S. Department of Defense offers a 50% price preference to U.S. domestic firms bidding to supply Defense Department purchases. Non-U.S. bidders are at a daunting bidding disadvantage in this situation. Foreign bidders are discriminated in favor of the substantially preferred domestic U.S. firms. If any domestic U.S. supplier's bid is no more than 50% higher than the lowest foreign bid, the domestic bid is accepted. In other words, the preferred domestic U.S. supplier wins in any such auction with the U.S. Department of Defense against a foreign bidder who, without the preference policy in place, would win with a bid of nearly half the sale price.
In business-to-business settings, often less legislatively constrained than governmental market situations, such preferential treatment of some suppliers, with corresponding discrimination against others, is even more prevalent. Similarly, in many business-to-consumer situations, a seller may wish to treat some segment of customers, sharing some particular trait, differently from others. For example, certain customers may be treated preferentially by businesses and other customers discriminatorily.
Illustratively, “loyal” customers, e.g., customers with frequent or repeated significant orders, bidders with better bidder ranking criteria, e.g., higher eBay® ratings, and customers with identifiably more elastic demands, etc., may be treated preferentially by awarding them a price discount. Similarly, mortgage customers or other borrowers with excellent credit ratings may be awarded a lower interest rate. Conversely, new, e.g., unknown customers, inflexibly rigid customers with stringent accommodation demands, or borrowers with lower credit ratings may represent to a business a higher cost or degree of risk in dealing with them. Such riskier or costlier customers may be discriminated against with higher interest rates, requiring premium prices, or in other handicapping ways.
Setting price preference policies in markets, particularly in auctions, can improve the market outcome in favor of the policy setting market participant, and is thus an important, perhaps crucial business consideration. Currently, these decisions are made by auction participants on an ad hoc basis, sometimes with the assistance of consultants operating themselves on a more or less ad hoc basis. A high degree of uncertainty intrinsic in auction price preference policy related decision making often precludes optimal outcomes, because the soundness of a particular decision in a particular situation cannot be ascertained prior to observation of the outcome (e.g., after the transaction has taken place). Inexperienced auction participants often make unsophisticated sub-optimal decisions regarding the setting of a price preference policy. Experience and a host of other human elements may thus effect the soundness of decision making in a given auction price preference situation. Nevertheless, no conventional systematic auction price preference analytical decision making mechanism is available.
Currently, the decisions on the parameters of preference policy are left entirely to the person conducting the auction. There is little systemic data analysis to guide these decisions. Given the multiplicity of items bought/sold through auctions, it is typically too costly to hire expert analysis to configure the price-preference policies for each case. Typically, a given policy, say 10% preference for preferred suppliers, is applied to a large class of procurement situations. Yet bidders' cost distributions vary considerably across procurement items and across time. A fixed preference policy is rarely optimal for every case to which it is applied.
As is known, the outcome of an auction (e.g., who gets what, who pays how much) is determined by bidding behavior of bidders. Bidding behavior depends on, among other factors, the auction rules in that different auction rules induce different behavior on the part of the bidders. A bidder's behavior under a given collection of auction rules in turn is determined by the bidder's private information. The structure of the private information held by the bidders is thus a key factor in evaluating alternative auction rules. This fundamental element of the auction environment is not directly observable and has to be estimated from observable and available data.
There exists a need for an automated estimation and optimization solution for configuring the parameters of preference policies to be implemented in auctions. What is needed is a method and/or system that configures the optimal preference policies that can be combined with any auction format a market decision maker may wish to conduct. What is also needed is a method and/or system that applies to any auction participants, either buyers conducting an auction to procure an item, or a seller, conducting to sell an item, which estimate's bidders private information and correspondingly identifies exploitable asymmetries. Further, what is needed is a method and/or system that achieves the foregoing to implement a preferential treatment policy.